Google Research AI blog

APRIL 27, 2023

That is where we can use the ability of ML models to pick up on subtle intricate patterns in large amounts of data. We’ve previously demonstrated the ability to use ML models to quickly phenotype at scale for retinal diseases. We trained ML models to predict whether an individual has COPD using the full spirograms as inputs.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. So, let’s dive in and discover everything you need to know about model packaging in machine learning.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. We recently developed four more new models.

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KDnuggets

DECEMBER 16, 2019

It is an annual tradition for Xavier Amatriain to write a year-end retrospective of advances in AI/ML, and this year is no different. Gain an understanding of the important developments of the past year, as well as insights into what expect in 2020.

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ML @ CMU

NOVEMBER 7, 2024

We address the challenges of landmine risk estimation by enhancing existing datasets with rich relevant features, constructing a novel, robust, and interpretable ML model that outperforms standard and new baselines, and identifying cohesive hazard clusters under geographic and budgetary constraints.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Google Research AI blog

FEBRUARY 23, 2023

Commensurate with our mission to demonstrate these societal benefits , Google Research’s programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Machine learning is ideal for cases when you want to do a semi-routine task faster, with more accuracy, or at a far larger scale than is possible with other solutions.

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Mlearning.ai

JUNE 14, 2023

LLM Learning MindMap: Lucidspark Learning Large Language Models Here is a print friendly view of all the resources. Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube

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The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Back in 2019.

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Smart Data Collective

MARCH 26, 2021

Machine learning (ML) is an innovative tool that advances technology in every industry around the world. From the most subtle advances, like Netflix recommendations, to life-saving medical diagnostics or even writing content , machine learning facilitates it all. Machine learning mimics the human brain. Directions.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. In this post, we showed cost-efficient training of LLMs on AWS deep learning hardware. We’ll outline how we cost-effectively (3.2

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Pickl AI

JANUARY 3, 2024

Google, a tech powerhouse, offers insights into the upper echelons of ML salaries in the United States. As the market evolves, continuous learning and adaptability are crucial for success in this dynamic field. In 2024, the significance of Machine Learning (ML) cannot be overstated. billion in 2023 to an impressive $225.91

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Flipboard

JULY 10, 2023

Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ). Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ).

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AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We evaluated the WAPE for all BLs in the auto end market for 2019, 2020, and 2021.

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AWS Machine Learning Blog

MARCH 13, 2023

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. Baseline 1 2 3 4 5 mIoU 72.72

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Heartbeat

JUNE 14, 2023

Label Smoothing Equation [5] In their 2019 paper “ When does label smoothing help? ”, Hinton et al. [5] logits of the final layer), which helps reduce overconfidence and subsequently reduces the network’s ECE. ” Advances in neural information processing systems 32 (2019). [6] Measuring Calibration in Deep Learning.

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How to Learn Machine Learning

JULY 20, 2024

In this article you will learn about 7 of the top Generative AI Trends to watch out for in this year, so please please sit back relax, enjoy, and learn! It falls under machine learning and uses deep learning algorithms and programs to create music, art, and other creative content based on the user’s input.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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AWS Machine Learning Blog

JUNE 13, 2023

The size of the machine learning (ML) models––large language models ( LLMs ) and foundation models ( FMs )–– is growing fast year-over-year , and these models need faster and more powerful accelerators, especially for generative AI. With AWS Inferentia1, customers saw up to 2.3x It is natively integrated with PyTorch and TensorFlow.

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Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. What is RoBERTa?

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., 2019) or by using input pre-processing techniques to remove adversarial perturbations (Xie et al.,

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. ML practitioners, believing they had to match the sheer size of ImageNet, refrained from pre-training with much smaller available medical image datasets, let alone developing new ones.

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Heartbeat

MARCH 22, 2023

ML models use loss functions to help choose the model that is creating the best model fit for a given set of data (actual values are the most like the estimated values). Train Your Own YoloV7 Object Detection Model — by Gourav Bais Object detection is one of the most important concepts in the deep learning space.

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

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Heartbeat

OCTOBER 10, 2023

It was developed by Facebook AI Research and released in 2019. Interpretability: Like many other deep learning models, RoBERTa is frequently referred to as a “black box.” Overfitting: RoBERTa, like any deep learning model, is prone to overfitting. It is a state-of-the-art model for a variety of NLP tasks.

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Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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Heartbeat

JANUARY 23, 2023

2019) set out to determine how well an NLP model could determine whether or not a given sentence adhered to linguistic norms. Papers With Code showcases cutting-edge studies in Machine Learning with the source code needed to replicate the results. Alex Warstadt et al. SST-2 : The Stanford Sentiment Treebank dataset. You can see here.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

For instance, in 2021, we saw a significant increase in awareness of clinical research studies seeking volunteers, which was reported at 63% compared to 54% in 2019 by Applied Clinical Trials. With SageMaker, you can optimize your ML environment for sustainability.

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Heartbeat

SEPTEMBER 13, 2023

For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Captum allows users to explain both deep learning and traditional machine learning models. Why We Will Never Open Deep Learning's Black Box || Towards Data Science Brent, M.,

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Heartbeat

JUNE 6, 2023

Prerequisites To follow along with this tutorial, you will need the following: Basic knowledge of Python and deep learning. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Advances in neural information processing systems 32 (2019).

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Heartbeat

JUNE 28, 2023

In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT? ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. However, the model’s size and computational requirements can make it challenging to use in some applications.

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DrivenData Labs

DECEMBER 10, 2023

degree in AI and ML specialization from Gujarat University, earned in 2019. He has diligently refined his abilities in the development, deployment, and scaling of AI and ML models, offering substantial contributions to GenAI projects. His educational background includes a Master's in AI and ML from John Moorse University, UK.

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Mlearning.ai

JUNE 4, 2023

This will yield a new dataset the we can feed to any other standard Machine or Deep Learning model. Conclusions In this articles we showcased a basic pipeline using Scikit-LLM and had a glimpse on the performance of ChatGPT in a Zero-Shot learning task. However, much more is yet to come looking at its roadmap.

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AWS Machine Learning Blog

OCTOBER 11, 2024

This makes it a popular option for a vector database when using Amazon Bedrock Knowledge Bases, because it makes it straightforward to build modern machine learning (ML) augmented search experiences and generative AI applications without having to manage the underlying vector database infrastructure.

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